• DocumentCode
    67623
  • Title

    A Hyper-Heuristic Scheduling Algorithm for Cloud

  • Author

    Chun-Wei Tsai ; Wei-Cheng Huang ; Meng-Hsiu Chiang ; Ming-Chao Chiang ; Chu-Sing Yang

  • Author_Institution
    Dept. of Appl. Inf. & Multimedia, Chia Nan Univ. of Pharmacy & Sci., Tainan, Taiwan
  • Volume
    2
  • Issue
    2
  • fYear
    2014
  • fDate
    April-June 1 2014
  • Firstpage
    236
  • Lastpage
    250
  • Abstract
    Rule-based scheduling algorithms have been widely used on many cloud computing systems because they are simple and easy to implement. However, there is plenty of room to improve the performance of these algorithms, especially by using heuristic scheduling. As such, this paper presents a novel heuristic scheduling algorithm, called hyper-heuristic scheduling algorithm (HHSA), to find better scheduling solutions for cloud computing systems. The diversity detection and improvement detection operators are employed by the proposed algorithm to dynamically determine which low-level heuristic is to be used in finding better candidate solutions. To evaluate the performance of the proposed method, this study compares the proposed method with several state-of-the-art scheduling algorithms, by having all of them implemented on CloudSim (a simulator) and Hadoop (a real system). The results show that HHSA can significantly reduce the makespan of task scheduling compared with the other scheduling algorithms evaluated in this paper, on both CloudSim and Hadoop.
  • Keywords
    cloud computing; knowledge based systems; scheduling; CloudSim; HHSA; Hadoop; cloud computing systems; hyper-heuristic scheduling algorithm; rule-based scheduling algorithms; Cloud computing; Heuristic algorithms; Pricing; Scheduling algorithms; Time complexity; Cloud computing; and Hadoop; evolutionary algorithm; scheduling;
  • fLanguage
    English
  • Journal_Title
    Cloud Computing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2168-7161
  • Type

    jour

  • DOI
    10.1109/TCC.2014.2315797
  • Filename
    6784130